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The Effects of Embodiment and Personality Expression on Learning in LLM-based Educational Agents

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arxiv 2407.10993 v1 pith:MKAMDSDD submitted 2024-06-24 cs.CL cs.GR

Sinan Sonlu , Bennie Bendiksen , Funda Durupinar , Uğur Güdükbay This is my paper
classification cs.CLcs.GR
keywords personalitylearningmodeldialogueeducationalmodelsperceivedtraits
verification ladder T0 review T1 audit T2 compute T3 formal
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This work investigates how personality expression and embodiment affect personality perception and learning in educational conversational agents. We extend an existing personality-driven conversational agent framework by integrating LLM-based conversation support tailored to an educational application. We describe a user study built on this system to evaluate two distinct personality styles: high extroversion and agreeableness and low extroversion and agreeableness. For each personality style, we assess three models: (1) a dialogue-only model that conveys personality through dialogue, (2) an animated human model that expresses personality solely through dialogue, and (3) an animated human model that expresses personality through both dialogue and body and facial animations. The results indicate that all models are positively perceived regarding both personality and learning outcomes. Models with high personality traits are perceived as more engaging than those with low personality traits. We provide a comprehensive quantitative and qualitative analysis of perceived personality traits, learning parameters, and user experiences based on participant ratings of the model types and personality styles, as well as users' responses to open-ended questions.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. ConversAR: Exploring Embodied LLM-Powered Group Conversations in Augmented Reality for Second Language Learners

    cs.HC 2025-05 conditional novelty 6.0 of 10

    Users of an AR app with two embodied LLM agents reported reduced speaking anxiety and increased autonomy for L2 group conversation practice, based on a small self-report study.

  2. Promoting Online Safety by Simulating Unsafe Conversations with LLMs

    cs.HC 2025-07 conditional novelty 4.0 of 10

    A pair of language models, one acting as scammer and one as target, can simulate realistic scam conversations, but the paper presents no user evaluation of whether this improves scam resilience.

  3. TRiMM: Transformer-Based Rich Motion Matching for Real-Time multi-modal Interaction in Digital Humans

    cs.GR 2025-06 conditional novelty 4.0 of 10

    TRiMM combines a sliding-window transformer with K-NN motion matching to synthesize co-speech gestures at 0.14 to 0.19 seconds per sentence on a consumer GPU.

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